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International Journal of Research in Engineering, Science and Management
Volume-1, Issue-11, November-2018
www.ijresm.com | ISSN (Online): 2581-5792
37
Abstract: The context of this paper is the control of autonomous
vehicles using fuzzy logic. The development of the autonomous
vehicles using fuzzy logic is considered to be one of the greatest
challenges. In this paper, we present the opportunities offered by
the fuzzy logic to control the autonomous vehicles. Real world
experiments will validate the approach. In building the fuzzy
hierarchical control of Autonomous Vehicles we use the language
known as FDTL (Fuzzy Decision Tree Language. Having said that,
it will be an important aspect in the era of automation.
Keywords: characteristics, Fuzzy logic, fuzzy controller, real
world
1. Introduction
Fuzzy logic is a powerful way to put engineering
methodology into products in a short amount of time. It’s very
useful in automotive engineering, where many system designs
involve the experience of design engineers as well as test
drivers. The automotive engineering is competitive on a huge
scale in various areas. In this article, We point out various
methods to make automatic using fuzzy logic .We show how
superior performance is achieved via fuzzy-logic and neural-
fuzzy design techniques. We also initiate various techniques,
tools, and code speed/size requirements. A Smart Car includes
modern technology like GPS detection, car positioning system,
speed control, obstruction detection, and many more but the
speed control is the most important and challenging aspect of a
Smart Car. In this paper, we reviewed the following ways to use
fuzzy logic in automative electronics:
Anti-braking system
A controller which can direct the vehicle safely
Smart car
2. Study of application of adaptive control in automobiles
An anti-lock braking system or ABS is a safety system which
prevents the wheels on a motor vehicle from locking up (or
ceasing to rotate) while braking. The aim of an ABS is to abate
brake distance while steer ability is retained even under hard
braking. Fuzzy logic in ABS: Fuzzy logic is a form of multi-
constraint logic derived from fuzzy set theory to deal with
reasoning that is approximate rather than precise. In contrast
with "crisp logic", where binary sets have binary logic, fuzzy
logic variables may have a truth value that between 0 and 1 and
is not constrained. Furthermore, when linguistic variables are
used, these degrees may be managed by specific functions. The
control of car and truck engines is becoming increasingly more
complex with more stringent emission standards and constant
effort to gain higher fuel efficiency. Twenty years ago, control
systems were mechanical (i.e., carburetor, distributor, and
breaker contact). Now, microcontroller-based systems control
fuel injection and ignition. The fuzzy ABS controller uses the
microprocessor together with the fuzzy coprocessor. Due to the
implementation of Fuzzy algorithms into the hardware of the
coprocessor, the calculation speed of the host processor
increased significantly. This offers facilities for implementation
of extended vehicle dynamics control.
The flexibility of the coprocessor is considerable, up to 64
rule bases are possible, each of them having up to 256 inputs
and rules. In addition, an interface to most commonly used
microprocessors are available. Arbitrary shapes of membership
functions, different de-fuzzification modes, an enormous rule
engine with up to 10 million rule calculations per second makes
this device a very interesting product in the field of real time
fuzzy control.
Fig. 1. Fuzzy controller architecture
Thus the concluding statement for the ABS fuzzy controller
system is basis of the controlling algorithm consists of a
nonlinear characteristic surface, which was created by fuzzy
logic. The convincing advantage of fuzzy logic is the ability to
modify and tune certain parts of this characteristic surface
easily and carefully. In this, linguistic rules or variables need to
be varied. This simplifies the development. This also shortens
the development time considerable. The deceleration level and
steer ability is comparable to commercially available systems.
Thus the ABS with FUZZY logic will help the driver to steer
while braking heavily and could prove to be lifesaving.
Fuzzy Logic in Automative Electronics
Mihir Kelkar1, Ruhi Sharma2, Sanika Chelari3
1Student, Department of Computer Science, DMCE, Airoli, India 2,3Student, Department of Mechatronics, NHITM, Thane, India
International Journal of Research in Engineering, Science and Management
Volume-1, Issue-11, November-2018
www.ijresm.com | ISSN (Online): 2581-5792
38
Fuzziness is a notion that makes the form of the rules closer
to the institutions of expert operations to whom the system
developer must turn. The cases often to develop the dynamic
predictive model which is very difficult. The proposed
controller comprises of the steering controller and the speed
controller. A good range of results are obtained for the
following real time systems.
Fig. 2. Obstacle avoidance in smart car
Here we consider an example, where the obstacle is placed
right after the curve, so the Ultra Sound sensors of the car detect
the obstacle too late. To not hit the obstacle, the car has to
decide for a very rapid turn. To optimize the steering effect, the
anti-skid controller must reduce the desired steering angle to the
maximum the road can take, avoiding both sliding and hitting
the obstacle. It can be represented with a diagram.
Fig. 3. Sensors in the car guide the vehicle on the road through fuzzy
The proposed design for this method is an Autonomous
vehicle always needs a navigation system to determine the
suitable paths to its target and a sensory system .This is
basically to acquire knowledge about the crossed environment.
To achieve most tasks of an automated vehicle the controller
must be able to adjust the steering angle and the velocity of the
vehicle simultaneously.
Traditional fuzzy systems allow output only the fuzzy
variables thereby restricting their context to low level systems.
The steering controller has the many objectives like as steering
the vehicle towards a specified target or steering the vehicle
around any obstacle and to stop at a desired location. The
velocity controller should depict the human behaviour as
slowing a vehicle when it comes near or over. The fuzzy
decision tree is used to express mapping from attribute values
to classes and consists of attribute nodes. It helps to visualize
the solution.
Thus in this way we can help analyze a particular solution.
The further implementations can be fabricated using various
changes.
3. Conclusion
Thus, we referred to several papers based on fuzzy logic. We
determined and reviewed the use of fuzzy logic in various
sections of Automative electronics. We have incorporated
various controllers, smart car and the use of anti-brake locking
system .This will help us in a long run. The fuzzy logic used
can be more precised and can achieve best moderations using
suitable variations.
References
[1] U. Anand, “Fuzzy Logic Vision And Control of Autonomous Vehicles,”
in IPASJ International Journal of Computer Science (IIJCS), vol. 4, no.
16, pp. 1-7, January 2016.
[2] A. Senapati, A. Maitra, S. Mondal, B. Bhattacharya, B. Kishore Mondal,,
and A. K. Kashyap, “Speed Control of Smart Car using Fuzzy Logic
Controller,” in International Journal of Advanced Research in Electrical,
Electronics and Instrumentation Engineering, vol. 7, no. 3, pp. 1121-
1129, March 2018.
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